Change data capture on no-master data stores

ABSTRACT

The present embodiments relate to implementing change data on no-master NoSQL data stores. An optimized node can be identified from a plurality of NoSQL data storage nodes and a specialized node can be connected (e.g., collocated) to the optimized node. The specialized node can maintain change data capture (CDC) data provided by client nodes in a hash map that can be used as a point of truth for coordinating CDC data across the plurality of NoSQL data storage nodes. The plurality of NoSQL data storage nodes can identify and coordinate all read/write data obtained from multiple client devices in a geographically separated large-scale (e.g., planet scale) system to identify change data in a distributed data store. The specialized data can provide read data to devices in the large-scale system to reconcile inconsistencies in change data across nodes in the large-scale system.

BACKGROUND

In many instances, change data capture (CDC) can be used with relationaldatabase management systems (RDBMSs). However, with a NoSQL data store,CDC may be loosely implemented using distributed log-based approaches(e.g., consensus algorithms). However, such approaches may not take intoaccount large-scale (e.g., global scale) no-master data stores, networkdelays in large-scale systems, or maintain consistency across the nodes.Particularly, read/writes can occur simultaneously or frequently acrossthe nodes in the large-scale and geographically separated stores (e.g.,planet scale stores), which can lead to inconsistencies of theread/writes.

Various techniques can be implemented to attempt to mitigateinconsistences of read/writes across a large-scale distributed or planetscale system. For example, tunable/eventual consistency algorithms(e.g., a Basic (B) Availability (A), Soft-state (S), Eventualconsistency (E) (or BASE) theorem; a network partitioning (P) in adistributed computer system, choosing between availability (A) andconsistency (C), but else (E), choosing between latency (L) andconsistency (C) (or PACELC) theorem). However, such approaches may notbe suited for implementation of CDC on very large geographicallydistributed no-master NoSQL data storage stores.

SUMMARY

The present embodiments relate to implementing change data capture (CDC)data across a large scale (e.g., planet scale) network of no-master datastores. A first exemplary embodiment provides a method. The method caninclude obtaining, from each of a plurality of client nodes in a networkenvironment, a time to obtain data packets from each of the plurality ofNoSQL data storage nodes. The method can also include identifying, fromthe obtained times to obtain data packets, a maximum time duration foreach of the plurality of NoSQL data storage nodes to provide the datapackets to each client node. The method can also include selecting asecond NoSQL data storage node as an optimized data storage node bydetermining that the second NoSQL data storage node has a lowest maximumtime duration to provide the data packets to any client node. Aspecialized node can be established at a computing device collocatedwith the first NoSQL data storage node.

The method can also include obtaining change data from a first clientnode of the plurality of client nodes. The method can also includeforwarding the change data to the second NoSQL data storage node. Thesecond NoSQL data storage node can be configured to provide the changedata to the specialized node containing a hash map mapping all changedata. The method can also include obtaining a change confirmation fromthe specialized node via the second NoSQL data storage node. The changeconfirmation can identify that the change data has been updated to thehash map.

In some embodiments, the change data is obtained from a second NoSQLdata storage node of the plurality of NoSQL data storage nodes. In someembodiments, the network environment includes a planet-scale networkenvironment. In some embodiments, the change data comprises change datacapture (CDC) data. In some embodiments, the method includes forwardingthe change confirmation to a third NoSQL data storage node coordinatethe change data across the plurality of NoSQL data storage nodes.

In some embodiments, the method includes detecting a triggering eventrelating to the first NoSQL data storage node. The method can alsoinclude, responsive to detecting the triggering event, obtaining, fromeach of the plurality of client nodes, updated times to obtain datapackets from each of the plurality of NoSQL data storage nodes. Themethod can also include identifying, from the obtained times to obtaindata packets, updated maximum time durations for each of the pluralityof NoSQL data storage nodes to provide the data packets to each clientnode. The method can also include selecting a fourth NoSQL data storagenode as the optimized data storage node responsive to determining thatthe fourth NoSQL data storage node has the lowest maximum time duration.

In some embodiments, the specialized node is a change data capture node,and the specialized node can be configured to periodically transfer aportion of hash map data to a disk persistent store and remove theportion of the hash map data from the hash map.

Another embodiment relates to a data storage node. The data storage nodecan include a processor and a computer-readable medium includinginstructions that, when executed by the processor, cause the processorto establish a specialized node collocated with the data storage node,the data storage node selected as an optimized data storage node. Theprocessor can also be caused to obtain change data capture (CDC) datafrom a first client node of a plurality of client nodes. The processorcan also be caused to forward the CDC data to the specialized node toupdate a hash map of change data with the obtained CDC data.

The processor can also be caused to receive a change confirmation fromthe specialized node, the change confirmation identifying that the CDCdata has been included in the hash map. The processor can also be causedto provide the change confirmation to other data storage nodes of aplurality of data storage nodes for coordination of the CDC data acrossthe plurality of data storage nodes.

In some embodiments, the non-transitory computer-readable medium furthercauses the processor to obtain, from each of the plurality of clientnodes, a time to obtain data packets from each of the plurality of datastorage nodes. The processor can also be caused to identify, from theobtained times to obtain data packets, a maximum time duration for eachof the plurality of data storage nodes to provide the data packets toeach client node. The processor can also be caused to select the datastorage node as the optimized data storage node responsive todetermining that the data storage node has a lowest maximum timeduration.

In some embodiments, the specialized node is a change data capture (CDC)node, and wherein the specialized node is configured to periodicallytransfer a portion of hash map data to a disk persistent store andremove the portion of the hash map data from the hash map. In someembodiments, the portion of hash map data is transferred to the diskpersistent store using least recently used (LRU) data replacement.

Another embodiment relates to a non-transitory computer-readable medium.The non-transitory computer-readable medium includes stored thereon asequence of instructions which, when executed by a processor causes theprocessor to execute a process. The process can include receiving, fromeach of a plurality of client nodes in a network environment, a time toobtain data packets from each of a plurality of NoSQL data storage nodesin the network environment. The process can also include deriving, foreach of the plurality of NoSQL data storage nodes, a time metricindicative a time to provide data packets to each of the plurality ofclient nodes. The process can also include selecting a first NoSQL datastorage node as an optimized data storage node based at least in part onthe time metrics, wherein a specialized node is established at acomputing device collocated with the first NoSQL data storage node.

The process can also include obtaining change data from a first clientnode of the plurality of client nodes. The process can also includeforwarding the change data to the first NoSQL data storage node, thefirst NoSQL data storage node configured to provide the change data tothe specialized node containing a hash map mapping all change data. Theprocess can also include obtaining a change confirmation from thespecialized node via the first NoSQL data storage node, the changeconfirmation identifying that the change data has been updated to thehash map.

In some embodiments, the time metric for each plurality of NoSQL datastorage nodes indicates a maximum time duration to provide data packetsto any of the plurality of client nodes, wherein selecting the firstNoSQL data storage node as the optimized data storage node includesidentifying that a first time metric for the first NoSQL data storagenode includes a lowest maximum time duration relative to any other timemetric for the plurality of NoSQL data storage nodes.

In some embodiments, the change data is obtained from an intermediateNoSQL data storage node of the plurality of NoSQL data storage nodes. Insome embodiments, the network environment includes a planet-scalenetwork environment. In some embodiments, the change data compriseschange data capture (CDC) data. In some embodiments, the process furthercomprises forwarding the change confirmation to a second NoSQL datastorage node to consistently provide the change data across theplurality of NoSQL data storage nodes.

In some embodiments, the process further comprises detecting atriggering event relating to the first NoSQL data storage node. Theprocess can also include, responsive to detecting the triggering event,obtaining, from each of the plurality of client nodes, updated times toobtain data packets from each of the plurality of NoSQL data storagenodes. The process can also include identifying, from the obtained timesto obtain data packets, updated maximum time durations for each of theplurality of NoSQL data storage nodes to provide the data packets toeach client node. The process can also include selecting a third NoSQLdata storage node as the optimized data storage node responsive todetermining that the third NoSQL data storage node has the lowestmaximum time duration.

In some embodiments, the triggering event includes any of detecting thata new client node is included in the network environment or determiningthat a time period for periodically updating the optimized data storagenode has expired. In some embodiments, the specialized node is a changedata capture node, and wherein the specialized node is configured toperiodically transfer a portion of hash map data to a disk persistentstore and remove the portion of the hash map data from the hash mapusing least recently used (LRU) data replacement.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 illustrates an example large-scale system including a pluralityof data storage nodes and client nodes, according to at least oneembodiment.

FIG. 2 is a signaling process illustrating a process for determining anoptimized node, according to at least one embodiment.

FIG. 3 illustrates a 3-Dimensional (3D) coordinate plane providing anumber of NoSQL storage nodes and client nodes as part of a system,according to at least one embodiment.

FIG. 4 is a signaling process for coordinating change data using aspecialized node, according to at least one embodiment.

FIG. 5 is a block diagram illustrating an example method forcoordinating change data across a plurality of NoSQL data storage nodes,according to at least one embodiment.

FIG. 6 is a block diagram illustrating one pattern for implementing acloud infrastructure as a service system, according to at least oneembodiment.

FIG. 7 is a block diagram illustrating another pattern for implementinga cloud infrastructure as a service system, according to at least oneembodiment.

FIG. 8 is a block diagram illustrating another pattern for implementinga cloud infrastructure as a service system, according to at least oneembodiment.

FIG. 9 is a block diagram illustrating another pattern for implementinga cloud infrastructure as a service system, according to at least oneembodiment.

FIG. 10 is a block diagram illustrating an example computer system,according to at least one embodiment.

DETAILED DESCRIPTION

The present embodiments relate to implementing change data on no-master(or NoSQL) data stores. Particularly, the present embodiments relate toidentifying an optimized node from a plurality of NoSQL data storagenodes and establishing a specialized node connected (e.g., collocated)to the optimized node. The specialized node can maintain change datacapture (CDC) data provided by client nodes in a hash map that can beused as a point of truth for coordinating CDC data across the pluralityof NoSQL data storage nodes.

A “no-master” data storage node as described herein can include astorage node (e.g., a computing device or virtual machine instanceimplementing one or more database instances) that is not associated witha coordinating (or “master”) node that is configured to providecoordination of data between nodes in a network. An example no-masterdata storage node can include a NoSQL data storage node. A NoSQL datastorage node as described herein can include a non-relational storagenode that can be non-tabular and can store data differently than nodesimplementing relational tables.

The present embodiments can identify and coordinate read/write data(e.g., all read-write data) obtained from multiple client devices in ageographically separated large-scale (e.g., planet scale, also known asglobal scale) system to identify change data in a distributed datastore. Clients may read from a specialized node established at anoptimized node that can be used to identify all change data. Thespecialized node can be set in a high availability (HA) mode and thespecialized node can be separated from the NoSQL data storage nodes(e.g., collocated with an optimized NoSQL data storage node. Thespecialized data can provide read data to devices in the large-scalesystem and update any change occurring across the nodes to act as thesingle point of truth for all data changes that have happened on theno-master data stores.

The specialized node can maintain a hash map of all changes identifiedacross the large-scale system. Further, to ensure consistency of changedata across nodes, a location of the specialized node can beperiodically re-calculated to determine whether to change thespecialized node to be associated with another NoSQL data storage node.The specialized node can store all change data for a period of time andperiodically move data to a persistent disk store and purge the data atthe specialized node.

As an illustrative example, a system can include a plurality ofnon-Structured Query Language (NoSQL) data storage nodes disposed acrossa region. For example, the NoSQL data storage nodes can be disposedabout the planet, with a first node in San Jose, a second node in NewYork, and a third node in New Delhi. The NoSQL data storage nodes andclient nodes can be connected via a plurality of networks across theregion.

A first node of the plurality of NoSQL data storage nodes can identifyother plurality of NoSQL data storage nodes and a geographic position(e.g., longitude, latitude, altitude) of each plurality of NoSQL datastorage node. The first node can also identify a plurality of clientnodes and geographic positions of the client nodes. The client nodes canbe configured to access data in the plurality of NoSQL data storagenodes or provide change data capture (CDC) data, for example.

Each client device can send a request for data from each of theplurality of NoSQL data storage nodes and identify a time to obtain datafrom each NoSQL data storage node. As network capabilities over a region(e.g., the planet) can differ, identifying a time to obtain data fromeach NoSQL data storage node can be utilized in identifying an optimizednode. For example, the optimized node can be selected as a node with alowest maximum time duration to provide data to any client node.

A specialized node can be established as connected (e.g., collocated)with the optimized NoSQL data storage node. For instance, a specializednode collocated with the optimized NoSQL data storage node can includethe specialized node being executed on one or more computing deviceswithin a threshold proximity (e.g., 100 meters) of computing devices ofthe optimized NoSQL data storage node, specialized node being executedon one or more computing devices in a same data center as computingdevices of the optimized NoSQL data storage node, or the specializednode being executed on one or more computing devices with acommunication latency below a threshold value with computing devices ofthe optimized NoSQL data storage node.

The specialized node can maintain a hash map to incorporate any changedata (CDC data) provided by client nodes. For example, a first clientnode can provide CDC data to a NoSQL data storage node and the NoSQLdata storage node can forward the CDC data to the specialized node viathe optimized NoSQL data storage node. The specialized node can update ahash map to include the CDC data to act as a point of truth for changedata across the system. The specialized node can notify the NoSQL datastorage nodes of the change data to coordinate change data across thesystem.

The present embodiments can improve accuracy of change data capturedacross a large-scale system and improve consistency of data across nodesin the system. Particularly, the specialized node can maintain arepository (e.g., hash map) of change data obtained from client nodesacross the system, providing a point of truth in coordinating changedata or reconciling any inconsistencies across the nodes. Further, theembodiments as described herein can mitigate problems in distributedcomputing systems (e.g., “Byzantine Faults”) by establishing aspecialized node to coordinate change data, rather than implementingdistributed consensus techniques. Additionally, the NoSQL data storagenodes are not required to update or modify a consistency mechanism ordesign, and the embodiments as described herein can be utilized with anyno-master data store types.

A. System Overview

FIG. 1 illustrates an example large-scale system 100 including aplurality of data storage nodes and client nodes. The system can includea plurality of data storage nodes (e.g., NoSQL data storage nodes)included as part of a large-scale (e.g., planet-scale) system.

Each data storage node can comprise one or more interconnected computingdevices disposed within an environment (e.g., a collocation center, datacenter). For example, in the example as illustrated in FIG. 1 , a firstNoSQL data storage node 102 a can be located in San Jose, a second NoSQLdata storage node 102 b can be located in New York, and a third NoSQLdata storage node 102 c can be located in New Delhi.

Additionally, the system 100 can include one or more client nodes 104a-c. Client nodes 104 a-c can include computing devices associated withoperators maintaining the data storage nodes or clients interacting withthe data storage nodes.

The nodes in the system can be connected via one or more networks.However, in many instances, networks connecting the nodes can havediffering capabilities (e.g., network throughput, bandwidthavailability) for data transmission. In addition to differing geographicdistances between nodes in the system, delays may exist in datatransmission across the nodes in the system. For example, datacommunication between a first data storage node in San Jose and a seconddata storage node in New York can be faster than data communicationbetween the second data storage node in New York and a third second datastorage node in New Delhi. The delays in data communication anddiffering communication times between nodes in the system 100 can leadto inconsistent coordination of change data between data storage nodesin the system 100.

In some embodiments, the nodes within the system 100 can be representedin a 3-dimensional (3D) space, with a longitude, latitude, and altitudebeing represented for each node. The dimensions of the nodes within the3D space can represent a location of all nodes in the system and canprovide insights into the geographic distances between the nodes in thesystem.

B. Optimized Node Identification

In the system comprising a plurality of no-master data storage nodes, anode can be identified as an optimized node. The optimized node caninclude a no-master data storage node with minimized data transferdelays to all other nodes in the system. The optimized node can have aspecialized node connected to (e.g., collocated with) the optimizednode.

FIG. 2 is a signaling process 200 illustrating a process for determiningan optimized node. As shown in FIG. 2 , a system can include a firstNoSQL storage node 202, a second NoSQL storage node 204, a first clientnode 206, and a second client node 208. The nodes within the system canestablish a connection by transferring data between nodes via one ormore networks.

At 212, the first client node 206 can request data from the first NoSQLstorage node 202. The request for data can include a null messagerequesting test data packets to track a time to obtain the test datapackets from the first NoSQL storage node. The first client nodeidentify a time to obtain the data from the first NoSQL storage node.

Similarly, at 214, the first client node 206 can request data from thesecond NoSQL storage node 204. The time to obtain data from the firstNoSQL storage node can differ from the time to obtain data from thesecond NoSQL storage node due to various factors, such as networkcapabilities, geographic distance between nodes, etc. The first clientnode can assemble a listing of times to obtain test data packets fromeach NoSQL storage node that can be sent to a NoSQL storage node asspecified in block 220.

At 216, a second client node 208 can request data from the first NoSQLstorage node 202. Similarly, at block 218, the second client node 208can request data from the second NoSQL storage node 204. The times toobtain data from the NoSQL storage nodes can be used to select anoptimized node as described below.

At 220, the first NoSQL storage node 202 can obtain the time to receivethe requested data from the NoSQL storage nodes from client node 1 202.Similarly, at block 222, the first NoSQL storage node 202 can obtain thetime to receive the requested data from the NoSQL storage nodes fromclient node 2 204. The times to receive the requested data from theNoSQL storage nodes can indicate a time, identified by each client node,to obtain the requested data from each NoSQL storage node. While thefirst NoSQL storage node can obtain the times to receive the requesteddata from the client nodes and determine an optimized node, any NoSQLstorage node can perform the steps as described herein.

At block 224, the first NoSQL storage node 202 can determine theoptimized node. The optimized node can include a node with a lowestdelay in data communication with the client nodes. The optimized nodecan be selected to have a specialized node established at a computingdevice connected to (e.g., collocated with) the first NoSQL storage nodeto maintain a listing of all change data for coordination of change dataacross the system.

The optimized node can be selected to include a NoSQL storage node witha lowest maximum time duration to provide data to each client device. Inother words, the optimized node can include a NoSQL storage node with ashortest time duration to provide test data packets to any client node.In some instances, all values included in the received time durationscan be sorted per NoSQL storage node, using sorting methods such as heapsort/merge sort in an ascending order. This can result in a maximum timethat any one client node took to obtain data from a NoSQL storage node.

In some embodiments, a dataset consisting of the max times derived fromthe received time data can be created. The dataset can be sorted to geta maximum times that was required to get the NoSQL storage node whichhad a lowest maximum time. As time is directly proportional to distanceand other distributed parameters, the least time taken to reach aparticular node among all the other nodes can be derived. The node withthe least time taken can include the optimized node in the cluster ofthe NoSQL nodes as described herein.

In some embodiments, an average time duration or a median time durationto provide data to each client device can be calculated and utilized indetermining an optimized node. A detailed example for determining anoptimized node is discussed with respect to FIG. 3 .

At 226, the first NoSQL storage node 202 can notify other NoSQL storagenodes of the optimized node. For example, responsive to determining thatthe first NoSQL storage node 202 is the optimized node, the first NoSQLstorage node 202 can forward an optimized node notification to thesecond NoSQL storage node 204.

FIG. 3 illustrates a 3-Dimensional (3D) coordinate plane 300 providing anumber of NoSQL storage nodes and client nodes as part of a system. Asshown in FIG. 3 , nodes included in the system can include NoSQL storagenodes N1, N2, N3, N4 and, client nodes C1, C2, C3, C4. The NoSQL storagenodes and client nodes can be represented in 3-D coordinates in thecoordinate plane representing a longitude, latitude, altitude of ageographic location of each node.

Each client node can determine a time to obtain data from each NoSQLstorage node. A time for each client device to obtain test data packetsfrom each NoSQL storage node can be derived by each client device. Forexample, a time for each client node to reach node N1 can include 5 ms,6 ms, 7 ms, and 8 ms; a time to reach node N2 by each client node caninclude 15 ms, 26 ms, 27 ms, and 9 ms; a time to reach node N3 by eachclient node can include 11 ms, 5 ms, 58 ms, and 9 ms; and a time toreach node N4 by each client node can include 10 ms, 34 ms, 38 ms, and56 ms.

In this example, a maximum time to provide data by each node can bedetermined. The maximum time as disclosed herein can comprise a metric(or a time metric) for each client node. For example, a maximum timeduration for N1 to provide data to any client node can include 8 ms;maximum time duration for N2 to provide data to any client node caninclude 27 ms; maximum time duration for N3 to provide data to anyclient node can include 58 ms; and a maximum time duration for N14 toprovide data to any client node can include 56 ms. The first NoSQLstorage node can compare the maximum time durations and determine thatthe maximum time duration for N1 (8 ms) is less than maximum timedurations for nodes N2 (27 ms), N3 (58 ms), and N4 (56 ms).

C. Coordinating Change Data Using a Specialized Node and an OptimizedNode

Responsive to determining the optimized node, a specialized node can beestablished as connected to the optimized node. The specialized node caninclude a module executing on one or more interconnected computingdevices collocated with the optimized node. The specialized node canmaintain a listing of all change data (CDC data) obtained from clientdevice(s) across the large-scale system and can provide a point of truthof all change data to reconcile any inconsistencies in change dataacross NoSQL storage nodes in the large-scale system.

FIG. 4 is a signaling process 400 for coordinating change data using aspecialized node. At 412, the client node 402 can provide change data(CDC data) to NoSQL storage node 404. While only one instance of changedata is provided in the example as illustrated in FIG. 4 , any number ofchange data instances can be obtained from a plurality of clientdevices.

At 414, the NoSQL storage node 404 can forward the change data to theoptimized node 406. Each NoSQL storage node can be configured to forwardchange data to the optimized node responsive to obtaining a notificationof the optimized node.

At 416, the optimized node 406 can provide the change data to thespecialized node 408. As noted above, the specialized node can include alisting of change data (e.g., a hash map) to maintain change dataobtained from client devices in the system.

At block 418, the specialized node 408 can update a hash map toincorporate the change data. The update to the hash map can map thechange data to a timestamp indicative of a time of obtaining the changedata, a client node providing the change data, a client associated withthe change data, etc. The hash map can provide a repository of allchange data obtained by the storage nodes in the system. Further, thehash map can provide a point of truth to reconcile any inconsistenciesin change data at various NoSQL storage nodes.

At 420, the specialized node 408 can send a change confirmation to theoptimized node 406 confirming the addition of the change data to thehash map. The optimized node 406 can update an internal listing/table ofchange data to incorporate the change data or confirm the addition ofthe change data to the internal listing/table of change data.

In some instances, the NoSQL storage nodes (e.g., NoSQL storage node404, optimized node 406) can include a NoSQL store with a consistencymodel (e.g., a tunable consistency model, eventual consistency model,strong consistency model). The consistency model can be used to providechange data across all nodes/tables/schemas/shards in the cluster ofNoSQL storage nodes. The consistency model can modify how the changedata is provided and stored across the NoSQL storage nodes in thenetwork.

At 422, the optimized node 406 can forward the change confirmation toother NoSQL storage nodes (e.g., 404). The NoSQL storage node 404 canalso update an internal listing/table of change data to incorporate thechange data or confirm the addition of the change data to the internallisting/table of change data. In some instances, if there is aninconsistency between the internal listing/table of change data and thechange data identified in the change confirmation, the NoSQL storagenode 404 can request the change data from the specialized node 408 andupdate the internal listing/table of change data to include therequested change data.

At 424, the specialized node 408 can periodically send a portion of thehash map data to a data store 410 (e.g., a persistent disk store). Thespecialized node 408 can periodically purge a portion of hash map datafrom the hash map to allow for efficient updating of the hash map withsubsequent change data. Initiating the removal of the hash map can beperiodic or based on the hash map including a threshold amount of data,for example.

At block 426, the specialized node 408 can remove the portion of thehash map data sent to the data store 410. This can be performedresponsive to sending the portion of the hash map data to the data store410 in block 424.

In some embodiments, the optimized node can be periodicallyre-calculated and transitioned to another node. For example, responsiveto detecting a change in the client nodes in the system (e.g., removalor addition of a client node), the optimized node calculation processdescribed above can be repeated. As another example, the optimized nodecalculation process can be periodically or randomly performed. If it isdetermined that the optimized node is to remain with the current NoSQLstorage node, no action may be taken. If it is determined that theoptimized is to change from the current NoSQL storage node to anotherNoSQL storage node, the specialized node can be removed at thecollocation with the current NoSQL storage node and the specialized nodecan be re-established at a collocation with the new NoSQL storage node.

FIG. 5 is a block diagram 500 illustrating an example method forcoordinating change data across a plurality of NoSQL data storage nodes.A network environment can include a planet-scale network environmentwith a plurality of NoSQL data storage nodes and a plurality of clientnodes. A first non-Structured Query Language (NoSQL) data storage nodeof the plurality of NoSQL data storage nodes can perform the method asdescribed herein.

At block 502, the first NoSQL data storage node can obtain, from each ofa plurality of client nodes in a network environment, a time to obtaindata packets from each of the plurality of NoSQL data storage nodes.

At block 504, the first NoSQL data storage node can identify a maximumtime duration for each of the plurality of NoSQL data storage nodes toprovide the data packets to each client node.

At block 506, the first NoSQL data storage node can select a secondNoSQL data storage node as an optimized data storage node by determiningthat the second NoSQL data storage node has a lowest maximum timeduration to provide the data packets to any client node. The specializednode can be established at a computing device collocated with the firstNoSQL data storage node responsive to selecting the second NoSQL datastorage node as the optimized data storage node.

The specialized node can include a change data capture node. Thespecialized node can be configured to periodically transfer a portion ofhash map data to a disk persistent store and remove the portion of thehash map data from the hash map.

At block 508, the first NoSQL data storage node can obtain change datafrom a first client node of the plurality of client nodes. The changedata can be obtained directly at first NoSQL data storage node orobtained from an intermediate NoSQL data storage node of the pluralityof NoSQL data storage nodes. The change data can include change datacapture (CDC) data.

At block 510, the first NoSQL data storage node can forward the changedata to the second NoSQL data storage node. The second NoSQL datastorage node can be configured to provide the change data to thespecialized node containing a hash map mapping all change data.

At block 512, the first NoSQL data storage node can obtain a changeconfirmation from the specialized node via the second NoSQL data storagenode. The change confirmation can identify that the change data has beenupdated to the hash map. In some embodiments, the first NoSQL datastorage node can forward the change confirmation to other NoSQL datastorage node (e.g., a third NoSQL data storage node) to coordinate thechange data across the plurality of NoSQL data storage nodes.

In some embodiments, the optimized node selection process as describedherein can be periodically re-calculated to determine whether to changethe optimized node to another NoSQL data storage node. For instance, thefirst NoSQL data storage node can detect a triggering event relating tothe second NoSQL data storage node. The triggering event can include anyof detecting that a new client node is included in the networkenvironment or determining that a time period for periodically updatingthe optimized data storage node has expired.

Responsive to detecting the triggering event, the first NoSQL datastorage node can obtain, from each of the plurality of client nodes,updated times to obtain data packets from each of the plurality of NoSQLdata storage nodes and identify updated maximum time durations for eachof the plurality of NoSQL data storage nodes to provide the data packetsto each client node. The first NoSQL data storage node can select athird NoSQL data storage node as the optimized data storage noderesponsive to determining that the third NoSQL data storage node has thelowest maximum time duration.

As noted above, infrastructure as a service (IaaS) is one particulartype of cloud computing. IaaS can be configured to provide virtualizedcomputing resources over a public network (e.g., the Internet). In anIaaS model, a cloud computing provider can host the infrastructurecomponents (e.g., servers, storage devices, network nodes (e.g.,hardware), deployment software, platform virtualization (e.g., ahypervisor layer), or the like). In some cases, an IaaS provider mayalso supply a variety of services to accompany those infrastructurecomponents (e.g., billing, monitoring, logging, load balancing andclustering, etc.). Thus, as these services may be policy-driven, IaaSusers may be able to implement policies to drive load balancing tomaintain application availability and performance.

In some instances, IaaS customers may access resources and servicesthrough a wide area network (WAN), such as the Internet, and can use thecloud provider's services to install the remaining elements of anapplication stack. For example, the user can log in to the IaaS platformto create virtual machines (VMs), install operating systems (OSs) oneach VM, deploy middleware such as databases, create storage buckets forworkloads and backups, and even install enterprise software into thatVM. Customers can then use the provider's services to perform variousfunctions, including balancing network traffic, troubleshootingapplication issues, monitoring performance, managing disaster recovery,etc.

In most cases, a cloud computing model will require the participation ofa cloud provider. The cloud provider may, but need not be, a third-partyservice that specializes in providing (e.g., offering, renting, selling)IaaS. An entity might also opt to deploy a private cloud, becoming itsown provider of infrastructure services.

In some examples, IaaS deployment is the process of putting a newapplication, or a new version of an application, onto a preparedapplication server or the like. It may also include the process ofpreparing the server (e.g., installing libraries, daemons, etc.). Thisis often managed by the cloud provider, below the hypervisor layer(e.g., the servers, storage, network hardware, and virtualization).Thus, the customer may be responsible for handling (OS), middleware,and/or application deployment (e.g., on self-service virtual machines(e.g., that can be spun up on demand) or the like.

In some examples, IaaS provisioning may refer to acquiring computers orvirtual hosts for use, and even installing needed libraries or serviceson them. In most cases, deployment does not include provisioning, andthe provisioning may need to be performed first.

In some cases, there are two different challenges for IaaS provisioning.First, there is the initial challenge of provisioning the initial set ofinfrastructure before anything is running. Second, there is thechallenge of evolving the existing infrastructure (e.g., adding newservices, changing services, removing services, etc.) once everythinghas been provisioned. In some cases, these two challenges may beaddressed by enabling the configuration of the infrastructure to bedefined declaratively. In other words, the infrastructure (e.g., whatcomponents are needed and how they interact) can be defined by one ormore configuration files. Thus, the overall topology of theinfrastructure (e.g., what resources depend on which, and how they eachwork together) can be described declaratively. In some instances, oncethe topology is defined, a workflow can be generated that creates and/ormanages the different components described in the configuration files.

In some examples, an infrastructure may have many interconnectedelements. For example, there may be one or more virtual private clouds(VPCs) (e.g., a potentially on-demand pool of configurable and/or sharedcomputing resources), also known as a core network. In some examples,there may also be one or more inbound/outbound traffic group rulesprovisioned to define how the inbound and/or outbound traffic of thenetwork will be set up and one or more virtual machines (VMs). Otherinfrastructure elements may also be provisioned, such as a loadbalancer, a database, or the like. As more and more infrastructureelements are desired and/or added, the infrastructure may incrementallyevolve.

In some instances, continuous deployment techniques may be employed toenable deployment of infrastructure code across various virtualcomputing environments. Additionally, the described techniques canenable infrastructure management within these environments. In someexamples, service teams can write code that is desired to be deployed toone or more, but often many, different production environments (e.g.,across various different geographic locations, sometimes spanning theentire world). However, in some examples, the infrastructure on whichthe code will be deployed must first be set up. In some instances, theprovisioning can be done manually, a provisioning tool may be utilizedto provision the resources, and/or deployment tools may be utilized todeploy the code once the infrastructure is provisioned.

FIG. 6 is a block diagram 600 illustrating an example pattern of an IaaSarchitecture, according to at least one embodiment. Service operators602 can be communicatively coupled to a secure host tenancy 604 that caninclude a virtual cloud network (VCN) 606 and a secure host subnet 608.In some examples, the service operators 602 may be using one or moreclient computing devices, which may be portable handheld devices (e.g.,an iPhone®, cellular telephone, an iPad®, computing tablet, a personaldigital assistant (PDA)) or wearable devices (e.g., a Google Glass® headmounted display), running software such as Microsoft Windows Mobile®,and/or a variety of mobile operating systems such as iOS, Windows Phone,Android, BlackBerry 8, Palm OS, and the like, and being Internet,e-mail, short message service (SMS), Blackberry®, or other communicationprotocol enabled. Alternatively, the client computing devices can begeneral purpose personal computers including, by way of example,personal computers and/or laptop computers running various versions ofMicrosoft Windows®, Apple Macintosh®, and/or Linux operating systems.The client computing devices can be workstation computers running any ofa variety of commercially-available UNIX® or UNIX-like operatingsystems, including without limitation the variety of GNU/Linux operatingsystems, such as for example, Google Chrome OS. Alternatively, or inaddition, client computing devices may be any other electronic device,such as a thin-client computer, an Internet-enabled gaming system (e.g.,a Microsoft Xbox gaming console with or without a Kinect® gesture inputdevice), and/or a personal messaging device, capable of communicatingover a network that can access the VCN 606 and/or the Internet.

The VCN 606 can include a local peering gateway (LPG) 610 that can becommunicatively coupled to a secure shell (SSH) VCN 612 via an LPG 610contained in the SSH VCN 612. The SSH VCN 612 can include an SSH subnet614, and the SSH VCN 612 can be communicatively coupled to a controlplane VCN 616 via the LPG 610 contained in the control plane VCN 616.Also, the SSH VCN 612 can be communicatively coupled to a data plane VCN618 via an LPG 610. The control plane VCN 616 and the data plane VCN 618can be contained in a service tenancy 619 that can be owned and/oroperated by the IaaS provider.

The control plane VCN 616 can include a control plane demilitarized zone(DMZ) tier 620 that acts as a perimeter network (e.g., portions of acorporate network between the corporate intranet and external networks).The DMZ-based servers may have restricted responsibilities and help keepbreaches contained. Additionally, the DMZ tier 620 can include one ormore load balancer (LB) subnet(s) 622, a control plane app tier 624 thatcan include app subnet(s) 626, a control plane data tier 628 that caninclude database (DB) subnet(s) 630 (e.g., frontend DB subnet(s) and/orbackend DB subnet(s)). The LB subnet(s) 622 contained in the controlplane DMZ tier 620 can be communicatively coupled to the app subnet(s)626 contained in the control plane app tier 624 and an Internet gateway634 that can be contained in the control plane VCN 616, and the appsubnet(s) 626 can be communicatively coupled to the DB subnet(s) 630contained in the control plane data tier 628 and a service gateway 636and a network address translation (NAT) gateway 638. The control planeVCN 616 can include the service gateway 636 and the NAT gateway 638.

The control plane VCN 616 can include a data plane mirror app tier 640that can include app subnet(s) 626. The app subnet(s) 626 contained inthe data plane mirror app tier 640 can include a virtual networkinterface controller (VNIC) 642 that can execute a compute instance 644.The compute instance 644 can communicatively couple the app subnet(s)626 of the data plane mirror app tier 640 to app subnet(s) 626 that canbe contained in a data plane app tier 646.

The data plane VCN 618 can include the data plane app tier 646, a dataplane DMZ tier 648, and a data plane data tier 650. The data plane DMZtier 648 can include LB subnet(s) 622 that can be communicativelycoupled to the app subnet(s) 626 of the data plane app tier 646 and theInternet gateway 634 of the data plane VCN 618. The app subnet(s) 626can be communicatively coupled to the service gateway 636 of the dataplane VCN 618 and the NAT gateway 638 of the data plane VCN 618. Thedata plane data tier 650 can also include the DB subnet(s) 630 that canbe communicatively coupled to the app subnet(s) 626 of the data planeapp tier 646.

The Internet gateway 634 of the control plane VCN 616 and of the dataplane VCN 618 can be communicatively coupled to a metadata managementservice 652 that can be communicatively coupled to public Internet 654.Public Internet 654 can be communicatively coupled to the NAT gateway638 of the control plane VCN 616 and of the data plane VCN 618. Theservice gateway 636 of the control plane VCN 616 and of the data planeVCN 618 can be communicatively couple to cloud services 656.

In some examples, the service gateway 636 of the control plane VCN 616or of the data plane VCN 618 can make application programming interface(API) calls to cloud services 656 without going through public Internet654. The API calls to cloud services 656 from the service gateway 636can be one-way: the service gateway 636 can make API calls to cloudservices 656, and cloud services 656 can send requested data to theservice gateway 636. But, cloud services 656 may not initiate API callsto the service gateway 636.

In some examples, the secure host tenancy 604 can be directly connectedto the service tenancy 619, which may be otherwise isolated. The securehost subnet 608 can communicate with the SSH subnet 614 through an LPG610 that may enable two-way communication over an otherwise isolatedsystem. Connecting the secure host subnet 608 to the SSH subnet 614 maygive the secure host subnet 608 access to other entities within theservice tenancy 619.

The control plane VCN 616 may allow users of the service tenancy 619 toset up or otherwise provision desired resources. Desired resourcesprovisioned in the control plane VCN 616 may be deployed or otherwiseused in the data plane VCN 618. In some examples, the control plane VCN616 can be isolated from the data plane VCN 618, and the data planemirror app tier 640 of the control plane VCN 616 can communicate withthe data plane app tier 646 of the data plane VCN 618 via VNICs 642 thatcan be contained in the data plane mirror app tier 640 and the dataplane app tier 646.

In some examples, users of the system, or customers, can make requests,for example create, read, update, or delete (CRUD) operations, throughpublic Internet 654 that can communicate the requests to the metadatamanagement service 652. The metadata management service 652 cancommunicate the request to the control plane VCN 616 through theInternet gateway 634. The request can be received by the LB subnet(s)622 contained in the control plane DMZ tier 620. The LB subnet(s) 622may determine that the request is valid, and in response to thisdetermination, the LB subnet(s) 622 can transmit the request to appsubnet(s) 626 contained in the control plane app tier 624. If therequest is validated and requires a call to public Internet 654, thecall to public Internet 654 may be transmitted to the NAT gateway 638that can make the call to public Internet 654. Memory that may bedesired to be stored by the request can be stored in the DB subnet(s)630.

In some examples, the data plane mirror app tier 640 can facilitatedirect communication between the control plane VCN 616 and the dataplane VCN 618. For example, changes, updates, or other suitablemodifications to configuration may be desired to be applied to theresources contained in the data plane VCN 618. Via a VNIC 642, thecontrol plane VCN 616 can directly communicate with, and can therebyexecute the changes, updates, or other suitable modifications toconfiguration to, resources contained in the data plane VCN 618.

In some embodiments, the control plane VCN 616 and the data plane VCN618 can be contained in the service tenancy 619. In this case, the user,or the customer, of the system may not own or operate either the controlplane VCN 616 or the data plane VCN 618. Instead, the IaaS provider mayown or operate the control plane VCN 616 and the data plane VCN 618,both of which may be contained in the service tenancy 619. Thisembodiment can enable isolation of networks that may prevent users orcustomers from interacting with other users', or other customers',resources. Also, this embodiment may allow users or customers of thesystem to store databases privately without needing to rely on publicInternet 654, which may not have a desired level of threat prevention,for storage.

In other embodiments, the LB subnet(s) 622 contained in the controlplane VCN 616 can be configured to receive a signal from the servicegateway 636. In this embodiment, the control plane VCN 616 and the dataplane VCN 618 may be configured to be called by a customer of the IaaSprovider without calling public Internet 654. Customers of the IaaSprovider may desire this embodiment since database(s) that the customersuse may be controlled by the IaaS provider and may be stored on theservice tenancy 619, which may be isolated from public Internet 654.

FIG. 7 is a block diagram 700 illustrating another example pattern of anIaaS architecture, according to at least one embodiment. Serviceoperators 702 (e.g. service operators 602 of FIG. 6 ) can becommunicatively coupled to a secure host tenancy 704 (e.g. the securehost tenancy 604 of FIG. 6 ) that can include a virtual cloud network(VCN) 706 (e.g. the VCN 606 of FIG. 6 ) and a secure host subnet 708(e.g. the secure host subnet 608 of FIG. 6 ). The VCN 706 can include alocal peering gateway (LPG) 710 (e.g. the LPG 610 of FIG. 6 ) that canbe communicatively coupled to a secure shell (SSH) VCN 712 (e.g. the SSHVCN 612 of FIG. 6 ) via an LPG 610 contained in the SSH VCN 712. The SSHVCN 712 can include an SSH subnet 714 (e.g. the SSH subnet 614 of FIG. 6), and the SSH VCN 712 can be communicatively coupled to a control planeVCN 716 (e.g. the control plane VCN 616 of FIG. 6 ) via an LPG 710contained in the control plane VCN 716. The control plane VCN 716 can becontained in a service tenancy 719 (e.g. the service tenancy 619 of FIG.6 ), and the data plane VCN 718 (e.g. the data plane VCN 618 of FIG. 6 )can be contained in a customer tenancy 721 that may be owned or operatedby users, or customers, of the system.

The control plane VCN 716 can include a control plane DMZ tier 720 (e.g.the control plane DMZ tier 620 of FIG. 6 ) that can include LB subnet(s)722 (e.g. LB subnet(s) 622 of FIG. 6 ), a control plane app tier 724(e.g. the control plane app tier 624 of FIG. 6 ) that can include appsubnet(s) 726 (e.g. app subnet(s) 626 of FIG. 6 ), a control plane datatier 728 (e.g. the control plane data tier 628 of FIG. 6 ) that caninclude database (DB) subnet(s) 730 (e.g. similar to DB subnet(s) 630 ofFIG. 6 ). The LB subnet(s) 722 contained in the control plane DMZ tier720 can be communicatively coupled to the app subnet(s) 726 contained inthe control plane app tier 724 and an Internet gateway 734 (e.g. theInternet gateway 634 of FIG. 6 ) that can be contained in the controlplane VCN 716, and the app subnet(s) 726 can be communicatively coupledto the DB subnet(s) 730 contained in the control plane data tier 728 anda service gateway 736 (e.g. the service gateway of FIG. 6 ) and anetwork address translation (NAT) gateway 738 (e.g. the NAT gateway 638of FIG. 6 ). The control plane VCN 716 can include the service gateway736 and the NAT gateway 738.

The control plane VCN 716 can include a data plane mirror app tier 740(e.g. the data plane mirror app tier 640 of FIG. 6 ) that can includeapp subnet(s) 726. The app subnet(s) 726 contained in the data planemirror app tier 740 can include a virtual network interface controller(VNIC) 742 (e.g. the VNIC of 642) that can execute a compute instance744 (e.g. similar to the compute instance 644 of FIG. 6 ). The computeinstance 744 can facilitate communication between the app subnet(s) 726of the data plane mirror app tier 740 and the app subnet(s) 726 that canbe contained in a data plane app tier 746 (e.g. the data plane app tier646 of FIG. 6 ) via the VNIC 742 contained in the data plane mirror apptier 740 and the VNIC 742 contained in the data plane app tier 746.

The Internet gateway 734 contained in the control plane VCN 716 can becommunicatively coupled to a metadata management service 752 (e.g. themetadata management service 652 of FIG. 6 ) that can be communicativelycoupled to public Internet 754 (e.g. public Internet 654 of FIG. 6 ).Public Internet 754 can be communicatively coupled to the NAT gateway738 contained in the control plane VCN 716. The service gateway 736contained in the control plane VCN 716 can be communicatively couple tocloud services 756 (e.g. cloud services 656 of FIG. 6 ).

In some examples, the data plane VCN 718 can be contained in thecustomer tenancy 721. In this case, the IaaS provider may provide thecontrol plane VCN 716 for each customer, and the IaaS provider may, foreach customer, set up a unique compute instance 744 that is contained inthe service tenancy 719. Each compute instance 744 may allowcommunication between the control plane VCN 716, contained in theservice tenancy 719, and the data plane VCN 718 that is contained in thecustomer tenancy 721. The compute instance 744 may allow resources, thatare provisioned in the control plane VCN 716 that is contained in theservice tenancy 719, to be deployed or otherwise used in the data planeVCN 718 that is contained in the customer tenancy 721.

In other examples, the customer of the IaaS provider may have databasesthat live in the customer tenancy 721. In this example, the controlplane VCN 716 can include the data plane mirror app tier 740 that caninclude app subnet(s) 726. The data plane mirror app tier 740 can residein the data plane VCN 718, but the data plane mirror app tier 740 maynot live in the data plane VCN 718. That is, the data plane mirror apptier 740 may have access to the customer tenancy 721, but the data planemirror app tier 740 may not exist in the data plane VCN 718 or be ownedor operated by the customer of the IaaS provider. The data plane mirrorapp tier 740 may be configured to make calls to the data plane VCN 718but may not be configured to make calls to any entity contained in thecontrol plane VCN 716. The customer may desire to deploy or otherwiseuse resources in the data plane VCN 718 that are provisioned in thecontrol plane VCN 716, and the data plane mirror app tier 740 canfacilitate the desired deployment, or other usage of resources, of thecustomer.

In some embodiments, the customer of the IaaS provider can apply filtersto the data plane VCN 718. In this embodiment, the customer candetermine what the data plane VCN 718 can access, and the customer mayrestrict access to public Internet 754 from the data plane VCN 718. TheIaaS provider may not be able to apply filters or otherwise controlaccess of the data plane VCN 718 to any outside networks or databases.Applying filters and controls by the customer onto the data plane VCN718, contained in the customer tenancy 721, can help isolate the dataplane VCN 718 from other customers and from public Internet 754.

In some embodiments, cloud services 756 can be called by the servicegateway 736 to access services that may not exist on public Internet754, on the control plane VCN 716, or on the data plane VCN 718. Theconnection between cloud services 756 and the control plane VCN 716 orthe data plane VCN 718 may not be live or continuous. Cloud services 756may exist on a different network owned or operated by the IaaS provider.Cloud services 756 may be configured to receive calls from the servicegateway 736 and may be configured to not receive calls from publicInternet 754. Some cloud services 756 may be isolated from other cloudservices 756, and the control plane VCN 716 may be isolated from cloudservices 756 that may not be in the same region as the control plane VCN716. For example, the control plane VCN 716 may be located in “Region1,” and cloud service “Deployment 6,” may be located in Region 1 and in“Region 2.” If a call to Deployment 6 is made by the service gateway 736contained in the control plane VCN 716 located in Region 1, the call maybe transmitted to Deployment 6 in Region 1. In this example, the controlplane VCN 716, or Deployment 6 in Region 1, may not be communicativelycoupled to, or otherwise in communication with, Deployment 6 in Region2.

FIG. 8 is a block diagram 800 illustrating another example pattern of anIaaS architecture, according to at least one embodiment. Serviceoperators 802 (e.g. service operators 602 of FIG. 6 ) can becommunicatively coupled to a secure host tenancy 804 (e.g. the securehost tenancy 604 of FIG. 6 ) that can include a virtual cloud network(VCN) 806 (e.g. the VCN 606 of FIG. 6 ) and a secure host subnet 808(e.g. the secure host subnet 608 of FIG. 6 ). The VCN 806 can include anLPG 810 (e.g. the LPG 610 of FIG. 6 ) that can be communicativelycoupled to an SSH VCN 812 (e.g. the SSH VCN 612 of FIG. 6 ) via an LPG810 contained in the SSH VCN 812. The SSH VCN 812 can include an SSHsubnet 814 (e.g. the SSH subnet 614 of FIG. 6 ), and the SSH VCN 812 canbe communicatively coupled to a control plane VCN 816 (e.g. the controlplane VCN 616 of FIG. 6 ) via an LPG 810 contained in the control planeVCN 816 and to a data plane VCN 818 (e.g. the data plane 618 of FIG. 6 )via an LPG 810 contained in the data plane VCN 818. The control planeVCN 816 and the data plane VCN 818 can be contained in a service tenancy819 (e.g. the service tenancy 619 of FIG. 6 ).

The control plane VCN 816 can include a control plane DMZ tier 820 (e.g.the control plane DMZ tier 620 of FIG. 6 ) that can include loadbalancer (LB) subnet(s) 822 (e.g. LB subnet(s) 622 of FIG. 6 ), acontrol plane app tier 824 (e.g. the control plane app tier 624 of FIG.6) that can include app subnet(s) 826 (e.g. similar to app subnet(s) 626of FIG. 6 ), a control plane data tier 828 (e.g. the control plane datatier 628 of FIG. 6 ) that can include DB subnet(s) 830. The LB subnet(s)822 contained in the control plane DMZ tier 820 can be communicativelycoupled to the app subnet(s) 826 contained in the control plane app tier824 and to an Internet gateway 834 (e.g. the Internet gateway 634 ofFIG. 6 ) that can be contained in the control plane VCN 816, and the appsubnet(s) 826 can be communicatively coupled to the DB subnet(s) 830contained in the control plane data tier 828 and to a service gateway836 (e.g. the service gateway of FIG. 6 ) and a network addresstranslation (NAT) gateway 838 (e.g. the NAT gateway 638 of FIG. 6 ). Thecontrol plane VCN 816 can include the service gateway 836 and the NATgateway 838.

The data plane VCN 818 can include a data plane app tier 846 (e.g. thedata plane app tier 646 of FIG. 6 ), a data plane DMZ tier 848 (e.g. thedata plane DMZ tier 648 of FIG. 6 ), and a data plane data tier 850(e.g. the data plane data tier 650 of FIG. 6 ). The data plane DMZ tier848 can include LB subnet(s) 822 that can be communicatively coupled totrusted app subnet(s) 860 and untrusted app subnet(s) 862 of the dataplane app tier 846 and the Internet gateway 834 contained in the dataplane VCN 818. The trusted app subnet(s) 860 can be communicativelycoupled to the service gateway 836 contained in the data plane VCN 818,the NAT gateway 838 contained in the data plane VCN 818, and DBsubnet(s) 830 contained in the data plane data tier 850. The untrustedapp subnet(s) 862 can be communicatively coupled to the service gateway836 contained in the data plane VCN 818 and DB subnet(s) 830 containedin the data plane data tier 850. The data plane data tier 850 caninclude DB subnet(s) 830 that can be communicatively coupled to theservice gateway 836 contained in the data plane VCN 818.

The untrusted app subnet(s) 862 can include one or more primary VNICs864(1)-(N) that can be communicatively coupled to tenant virtualmachines (VMs) 866(1)-(N). Each tenant VM 866(1)-(N) can becommunicatively coupled to a respective app subnet 867(1)-(N) that canbe contained in respective container egress VCNs 868(1)-(N) that can becontained in respective customer tenancies 870(1)-(N). Respectivesecondary VNICs 872(1)-(N) can facilitate communication between theuntrusted app subnet(s) 862 contained in the data plane VCN 818 and theapp subnet contained in the container egress VCNs 868(1)-(N). Eachcontainer egress VCNs 868(1)-(N) can include a NAT gateway 838 that canbe communicatively coupled to public Internet 854 (e.g. public Internet654 of FIG. 6 ).

The Internet gateway 834 contained in the control plane VCN 816 andcontained in the data plane VCN 818 can be communicatively coupled to ametadata management service 852 (e.g. the metadata management system 652of FIG. 6 ) that can be communicatively coupled to public Internet 854.Public Internet 854 can be communicatively coupled to the NAT gateway838 contained in the control plane VCN 816 and contained in the dataplane VCN 818. The service gateway 836 contained in the control planeVCN 816 and contained in the data plane VCN 818 can be communicativelycouple to cloud services 856.

In some embodiments, the data plane VCN 818 can be integrated withcustomer tenancies 870. This integration can be useful or desirable forcustomers of the IaaS provider in some cases such as a case that maydesire support when executing code. The customer may provide code to runthat may be destructive, may communicate with other customer resources,or may otherwise cause undesirable effects. In response to this, theIaaS provider may determine whether to run code given to the IaaSprovider by the customer.

In some examples, the customer of the IaaS provider may grant temporarynetwork access to the IaaS provider and request a function to beattached to the data plane tier app 846. Code to run the function may beexecuted in the VMs 866(1)-(N), and the code may not be configured torun anywhere else on the data plane VCN 818. Each VM 866(1)-(N) may beconnected to one customer tenancy 870. Respective containers 871(1)-(N)contained in the VMs 866(1)-(N) may be configured to run the code. Inthis case, there can be a dual isolation (e.g., the containers871(1)-(N) running code, where the containers 871(1)-(N) may becontained in at least the VM 866(1)-(N) that are contained in theuntrusted app subnet(s) 862), which may help prevent incorrect orotherwise undesirable code from damaging the network of the IaaSprovider or from damaging a network of a different customer. Thecontainers 871(1)-(N) may be communicatively coupled to the customertenancy 870 and may be configured to transmit or receive data from thecustomer tenancy 870. The containers 871(1)-(N) may not be configured totransmit or receive data from any other entity in the data plane VCN818. Upon completion of running the code, the IaaS provider may kill orotherwise dispose of the containers 871(1)-(N).

In some embodiments, the trusted app subnet(s) 860 may run code that maybe owned or operated by the IaaS provider. In this embodiment, thetrusted app subnet(s) 860 may be communicatively coupled to the DBsubnet(s) 830 and be configured to execute CRUD operations in the DBsubnet(s) 830. The untrusted app subnet(s) 862 may be communicativelycoupled to the DB subnet(s) 830, but in this embodiment, the untrustedapp subnet(s) may be configured to execute read operations in the DBsubnet(s) 830. The containers 871(1)-(N) that can be contained in the VM866(1)-(N) of each customer and that may run code from the customer maynot be communicatively coupled with the DB subnet(s) 830.

In other embodiments, the control plane VCN 816 and the data plane VCN818 may not be directly communicatively coupled. In this embodiment,there may be no direct communication between the control plane VCN 816and the data plane VCN 818. However, communication can occur indirectlythrough at least one method. An LPG 810 may be established by the IaaSprovider that can facilitate communication between the control plane VCN816 and the data plane VCN 818. In another example, the control planeVCN 816 or the data plane VCN 818 can make a call to cloud services 856via the service gateway 836. For example, a call to cloud services 856from the control plane VCN 816 can include a request for a service thatcan communicate with the data plane VCN 818.

FIG. 9 is a block diagram 900 illustrating another example pattern of anIaaS architecture, according to at least one embodiment. Serviceoperators 902 (e.g. service operators 602 of FIG. 6 ) can becommunicatively coupled to a secure host tenancy 904 (e.g. the securehost tenancy 604 of FIG. 6 ) that can include a virtual cloud network(VCN) 906 (e.g. the VCN 606 of FIG. 6 ) and a secure host subnet 908(e.g. the secure host subnet 608 of FIG. 6 ). The VCN 906 can include anLPG 910 (e.g. the LPG 610 of FIG. 6 ) that can be communicativelycoupled to an SSH VCN 912 (e.g. the SSH VCN 612 of FIG. 6 ) via an LPG910 contained in the SSH VCN 912. The SSH VCN 912 can include an SSHsubnet 914 (e.g. the SSH subnet 614 of FIG. 6 ), and the SSH VCN 912 canbe communicatively coupled to a control plane VCN 916 (e.g. the controlplane VCN 616 of FIG. 6 ) via an LPG 910 contained in the control planeVCN 916 and to a data plane VCN 918 (e.g. the data plane 618 of FIG. 6 )via an LPG 910 contained in the data plane VCN 918. The control planeVCN 916 and the data plane VCN 918 can be contained in a service tenancy919 (e.g. the service tenancy 619 of FIG. 6 ).

The control plane VCN 916 can include a control plane DMZ tier 920 (e.g.the control plane DMZ tier 620 of FIG. 6 ) that can include LB subnet(s)922 (e.g. LB subnet(s) 622 of FIG. 6 ), a control plane app tier 924(e.g. the control plane app tier 624 of FIG. 6 ) that can include appsubnet(s) 926 (e.g. app subnet(s) 626 of FIG. 6 ), a control plane datatier 928 (e.g. the control plane data tier 628 of FIG. 6 ) that caninclude DB subnet(s) 930 (e.g. DB subnet(s) 830 of FIG. 8 ). The LBsubnet(s) 922 contained in the control plane DMZ tier 920 can becommunicatively coupled to the app subnet(s) 926 contained in thecontrol plane app tier 924 and to an Internet gateway 934 (e.g. theInternet gateway 634 of FIG. 6 ) that can be contained in the controlplane VCN 916, and the app subnet(s) 926 can be communicatively coupledto the DB subnet(s) 930 contained in the control plane data tier 928 andto a service gateway 936 (e.g. the service gateway of FIG. 6 ) and anetwork address translation (NAT) gateway 938 (e.g. the NAT gateway 638of FIG. 6 ). The control plane VCN 916 can include the service gateway936 and the NAT gateway 938.

The data plane VCN 918 can include a data plane app tier 946 (e.g. thedata plane app tier 646 of FIG. 6 ), a data plane DMZ tier 948 (e.g. thedata plane DMZ tier 648 of FIG. 6 ), and a data plane data tier 950(e.g. the data plane data tier 650 of FIG. 6 ). The data plane DMZ tier948 can include LB subnet(s) 922 that can be communicatively coupled totrusted app subnet(s) 960 (e.g. trusted app subnet(s) 860 of FIG. 8 )and untrusted app subnet(s) 962 (e.g. untrusted app subnet(s) 862 ofFIG. 8 ) of the data plane app tier 946 and the Internet gateway 934contained in the data plane VCN 918. The trusted app subnet(s) 960 canbe communicatively coupled to the service gateway 936 contained in thedata plane VCN 918, the NAT gateway 938 contained in the data plane VCN918, and DB subnet(s) 930 contained in the data plane data tier 950. Theuntrusted app subnet(s) 962 can be communicatively coupled to theservice gateway 936 contained in the data plane VCN 918 and DB subnet(s)930 contained in the data plane data tier 950. The data plane data tier950 can include DB subnet(s) 930 that can be communicatively coupled tothe service gateway 936 contained in the data plane VCN 918.

The untrusted app subnet(s) 962 can include primary VNICs 964(1)-(N)that can be communicatively coupled to tenant virtual machines (VMs)966(1)-(N) residing within the untrusted app subnet(s) 962. Each tenantVM 966(1)-(N) can run code in a respective container 967(1)-(N), and becommunicatively coupled to an app subnet 926 that can be contained in adata plane app tier 946 that can be contained in a container egress VCN968. Respective secondary VNICs 972(1)-(N) can facilitate communicationbetween the untrusted app subnet(s) 962 contained in the data plane VCN918 and the app subnet contained in the container egress VCN 968. Thecontainer egress VCN can include a NAT gateway 938 that can becommunicatively coupled to public Internet 954 (e.g. public Internet 654of FIG. 6 ).

The Internet gateway 934 contained in the control plane VCN 916 andcontained in the data plane VCN 918 can be communicatively coupled to ametadata management service 952 (e.g. the metadata management system 652of FIG. 6 ) that can be communicatively coupled to public Internet 954.Public Internet 954 can be communicatively coupled to the NAT gateway938 contained in the control plane VCN 916 and contained in the dataplane VCN 918. The service gateway 936 contained in the control planeVCN 916 and contained in the data plane VCN 918 can be communicativelycouple to cloud services 956.

In some examples, the pattern illustrated by the architecture of blockdiagram 900 of FIG. 9 may be considered an exception to the patternillustrated by the architecture of block diagram 800 of FIG. 8 and maybe desirable for a customer of the IaaS provider if the IaaS providercannot directly communicate with the customer (e.g., a disconnectedregion). The respective containers 967(1)-(N) that are contained in theVMs 966(1)-(N) for each customer can be accessed in real-time by thecustomer. The containers 967(1)-(N) may be configured to make calls torespective secondary VNICs 972(1)-(N) contained in app subnet(s) 926 ofthe data plane app tier 946 that can be contained in the containeregress VCN 968. The secondary VNICs 972(1)-(N) can transmit the calls tothe NAT gateway 938 that may transmit the calls to public Internet 954.In this example, the containers 967(1)-(N) that can be accessed inreal-time by the customer can be isolated from the control plane VCN 916and can be isolated from other entities contained in the data plane VCN918. The containers 967(1)-(N) may also be isolated from resources fromother customers.

In other examples, the customer can use the containers 967(1)-(N) tocall cloud services 956. In this example, the customer may run code inthe containers 967(1)-(N) that requests a service from cloud services956. The containers 967(1)-(N) can transmit this request to thesecondary VNICs 972(1)-(N) that can transmit the request to the NATgateway that can transmit the request to public Internet 954. PublicInternet 954 can transmit the request to LB subnet(s) 922 contained inthe control plane VCN 916 via the Internet gateway 934. In response todetermining the request is valid, the LB subnet(s) can transmit therequest to app subnet(s) 926 that can transmit the request to cloudservices 956 via the service gateway 936.

It should be appreciated that IaaS architectures 600, 700, 800, 900depicted in the figures may have other components than those depicted.Further, the embodiments shown in the figures are only some examples ofa cloud infrastructure system that may incorporate an embodiment of thedisclosure. In some other embodiments, the IaaS systems may have more orfewer components than shown in the figures, may combine two or morecomponents, or may have a different configuration or arrangement ofcomponents.

In certain embodiments, the IaaS systems described herein may include asuite of applications, middleware, and database service offerings thatare delivered to a customer in a self-service, subscription-based,elastically scalable, reliable, highly available, and secure manner. Anexample of such an IaaS system is the Oracle Cloud Infrastructure (OCI)provided by the present assignee.

FIG. 10 illustrates an example computer system 1000, in which variousembodiments may be implemented. The system 1000 may be used to implementany of the computer systems described above. As shown in the figure,computer system 1000 includes a processing unit 1004 that communicateswith a number of peripheral subsystems via a bus subsystem 1002. Theseperipheral subsystems may include a processing acceleration unit 1006,an I/O subsystem 1008, a storage subsystem 1018 and a communicationssubsystem 1024. Storage subsystem 1018 includes tangiblecomputer-readable storage media 1022 and a system memory 1010.

Bus subsystem 1002 provides a mechanism for letting the variouscomponents and subsystems of computer system 1000 communicate with eachother as intended. Although bus subsystem 1002 is shown schematically asa single bus, alternative embodiments of the bus subsystem may utilizemultiple buses. Bus subsystem 1002 may be any of several types of busstructures including a memory bus or memory controller, a peripheralbus, and a local bus using any of a variety of bus architectures. Forexample, such architectures may include an Industry StandardArchitecture (ISA) bus, Micro Channel Architecture (MCA) bus, EnhancedISA (EISA) bus, Video Electronics Standards Association (VESA) localbus, and Peripheral Component Interconnect (PCI) bus, which can beimplemented as a Mezzanine bus manufactured to the IEEE P1386.1standard.

Processing unit 1004, which can be implemented as one or more integratedcircuits (e.g., a conventional microprocessor or microcontroller),controls the operation of computer system 1000. One or more processorsmay be included in processing unit 1004. These processors may includesingle core or multicore processors. In certain embodiments, processingunit 1004 may be implemented as one or more independent processing units1032 and/or 1034 with single or multicore processors included in eachprocessing unit. In other embodiments, processing unit 1004 may also beimplemented as a quad-core processing unit formed by integrating twodual-core processors into a single chip.

In various embodiments, processing unit 1004 can execute a variety ofprograms in response to program code and can maintain multipleconcurrently executing programs or processes. At any given time, some orall of the program code to be executed can be resident in processor(s)1004 and/or in storage subsystem 1018. Through suitable programming,processor(s) 1004 can provide various functionalities described above.Computer system 1000 may additionally include a processing accelerationunit 1006, which can include a digital signal processor (DSP), aspecial-purpose processor, and/or the like.

I/O subsystem 1008 may include user interface input devices and userinterface output devices. User interface input devices may include akeyboard, pointing devices such as a mouse or trackball, a touchpad ortouch screen incorporated into a display, a scroll wheel, a click wheel,a dial, a button, a switch, a keypad, audio input devices with voicecommand recognition systems, microphones, and other types of inputdevices. User interface input devices may include, for example, motionsensing and/or gesture recognition devices such as the Microsoft Kinect®motion sensor that enables users to control and interact with an inputdevice, such as the Microsoft Xbox® 360 game controller, through anatural user interface using gestures and spoken commands. Userinterface input devices may also include eye gesture recognition devicessuch as the Google Glass® blink detector that detects eye activity(e.g., ‘blinking’ while taking pictures and/or making a menu selection)from users and transforms the eye gestures as input into an input device(e.g., Google Glass®). Additionally, user interface input devices mayinclude voice recognition sensing devices that enable users to interactwith voice recognition systems (e.g., Siri® navigator), through voicecommands.

User interface input devices may also include, without limitation, threedimensional (3D) mice, joysticks or pointing sticks, gamepads andgraphic tablets, and audio/visual devices such as speakers, digitalcameras, digital camcorders, portable media players, webcams, imagescanners, fingerprint scanners, barcode reader 3D scanners, 3D printers,laser rangefinders, and eye gaze tracking devices. Additionally, userinterface input devices may include, for example, medical imaging inputdevices such as computed tomography, magnetic resonance imaging,position emission tomography, medical ultrasonography devices. Userinterface input devices may also include, for example, audio inputdevices such as MIDI keyboards, digital musical instruments and thelike.

User interface output devices may include a display subsystem, indicatorlights, or non-visual displays such as audio output devices, etc. Thedisplay subsystem may be a cathode ray tube (CRT), a flat-panel device,such as that using a liquid crystal display (LCD) or plasma display, aprojection device, a touch screen, and the like. In general, use of theterm “output device” is intended to include all possible types ofdevices and mechanisms for outputting information from computer system1000 to a user or other computer. For example, user interface outputdevices may include, without limitation, a variety of display devicesthat visually convey text, graphics and audio/video information such asmonitors, printers, speakers, headphones, automotive navigation systems,plotters, voice output devices, and modems.

Computer system 1000 may comprise a storage subsystem 1018 thatcomprises software elements, shown as being currently located within asystem memory 1010. System memory 1010 may store program instructionsthat are loadable and executable on processing unit 1004, as well asdata generated during the execution of these programs.

Depending on the configuration and type of computer system 1000, systemmemory 1010 may be volatile (such as random access memory (RAM)) and/ornon-volatile (such as read-only memory (ROM), flash memory, etc.) TheRAM typically contains data and/or program modules that are immediatelyaccessible to and/or presently being operated and executed by processingunit 1004. In some implementations, system memory 1010 may includemultiple different types of memory, such as static random access memory(SRAM) or dynamic random access memory (DRAM). In some implementations,a basic input/output system (BIOS), containing the basic routines thathelp to transfer information between elements within computer system1000, such as during start-up, may typically be stored in the ROM. Byway of example, and not limitation, system memory 1010 also illustratesapplication programs 1012, which may include client applications, Webbrowsers, mid-tier applications, relational database management systems(RDBMS), etc., program data 1014, and an operating system 1016. By wayof example, operating system 1016 may include various versions ofMicrosoft Windows®, Apple Macintosh®, and/or Linux operating systems, avariety of commercially-available UNIX® or UNIX-like operating systems(including without limitation the variety of GNU/Linux operatingsystems, the Google Chrome® OS, and the like) and/or mobile operatingsystems such as iOS, Windows® Phone, Android® OS, BlackBerry® 10 OS, andPalm® OS operating systems.

Storage subsystem 1018 may also provide a tangible computer-readablestorage medium for storing the basic programming and data constructsthat provide the functionality of some embodiments. Software (programs,code modules, instructions) that when executed by a processor providethe functionality described above may be stored in storage subsystem1018. These software modules or instructions may be executed byprocessing unit 1004. Storage subsystem 1018 may also provide arepository for storing data used in accordance with the presentdisclosure.

Storage subsystem 1000 may also include a computer-readable storagemedia reader 1020 that can further be connected to computer-readablestorage media 1022. Together and, optionally, in combination with systemmemory 1010, computer-readable storage media 1022 may comprehensivelyrepresent remote, local, fixed, and/or removable storage devices plusstorage media for temporarily and/or more permanently containing,storing, transmitting, and retrieving computer-readable information.

Computer-readable storage media 1022 containing code, or portions ofcode, can also include any appropriate media known or used in the art,including storage media and communication media, such as but not limitedto, volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage and/or transmissionof information. This can include tangible computer-readable storagemedia such as RAM, ROM, electronically erasable programmable ROM(EEPROM), flash memory or other memory technology, CD-ROM, digitalversatile disk (DVD), or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or other tangible computer readable media. This can also includenontangible computer-readable media, such as data signals, datatransmissions, or any other medium which can be used to transmit thedesired information and which can be accessed by computing system 1000.

By way of example, computer-readable storage media 1022 may include ahard disk drive that reads from or writes to non-removable, nonvolatilemagnetic media, a magnetic disk drive that reads from or writes to aremovable, nonvolatile magnetic disk, and an optical disk drive thatreads from or writes to a removable, nonvolatile optical disk such as aCD ROM, DVD, and Blu-Ray® disk, or other optical media.Computer-readable storage media 1022 may include, but is not limited to,Zip® drives, flash memory cards, universal serial bus (USB) flashdrives, secure digital (SD) cards, DVD disks, digital video tape, andthe like. Computer-readable storage media 1022 may also include,solid-state drives (SSD) based on non-volatile memory such asflash-memory based SSDs, enterprise flash drives, solid state ROM, andthe like, SSDs based on volatile memory such as solid state RAM, dynamicRAM, static RAM, DRAM-based SSDs, magnetoresistive RAM (MRAM) SSDs, andhybrid SSDs that use a combination of DRAM and flash memory based SSDs.The disk drives and their associated computer-readable media may providenon-volatile storage of computer-readable instructions, data structures,program modules, and other data for computer system 1000.

Communications subsystem 1024 provides an interface to other computersystems and networks. Communications subsystem 1024 serves as aninterface for receiving data from and transmitting data to other systemsfrom computer system 1000. For example, communications subsystem 1024may enable computer system 1000 to connect to one or more devices viathe Internet. In some embodiments communications subsystem 1024 caninclude radio frequency (RF) transceiver components for accessingwireless voice and/or data networks (e.g., using cellular telephonetechnology, advanced data network technology, such as 3G, 4G or EDGE(enhanced data rates for global evolution), WiFi (IEEE 802.11 familystandards, or other mobile communication technologies, or anycombination thereof), global positioning system (GPS) receivercomponents, and/or other components. In some embodiments communicationssubsystem 1024 can provide wired network connectivity (e.g., Ethernet)in addition to or instead of a wireless interface.

In some embodiments, communications subsystem 1024 may also receiveinput communication in the form of structured and/or unstructured datafeeds 1026, event streams 1028, event updates 1030, and the like onbehalf of one or more users who may use computer system 1000.

By way of example, communications subsystem 1024 may be configured toreceive data feeds 1026 in real-time from users of social networksand/or other communication services such as Twitter® feeds, Facebook®updates, web feeds such as Rich Site Summary (RSS) feeds, and/orreal-time updates from one or more third party information sources.

Additionally, communications subsystem 1024 may also be configured toreceive data in the form of continuous data streams, which may includeevent streams 1028 of real-time events and/or event updates 1030, thatmay be continuous or unbounded in nature with no explicit end. Examplesof applications that generate continuous data may include, for example,sensor data applications, financial tickers, network performancemeasuring tools (e.g. network monitoring and traffic managementapplications), clickstream analysis tools, automobile trafficmonitoring, and the like.

Communications subsystem 1024 may also be configured to output thestructured and/or unstructured data feeds 1026, event streams 1028,event updates 1030, and the like to one or more databases that may be incommunication with one or more streaming data source computers coupledto computer system 1000.

Computer system 1000 can be one of various types, including a handheldportable device (e.g., an iPhone® cellular phone, an iPad® computingtablet, a PDA), a wearable device (e.g., a Google Glass® head mounteddisplay), a PC, a workstation, a mainframe, a kiosk, a server rack, orany other data processing system.

Due to the ever-changing nature of computers and networks, thedescription of computer system 1000 depicted in the figure is intendedonly as a specific example. Many other configurations having more orfewer components than the system depicted in the figure are possible.For example, customized hardware might also be used and/or particularelements might be implemented in hardware, firmware, software (includingapplets), or a combination. Further, connection to other computingdevices, such as network input/output devices, may be employed. Based onthe disclosure and teachings provided herein, a person of ordinary skillin the art will appreciate other ways and/or methods to implement thevarious embodiments.

Although specific embodiments have been described, variousmodifications, alterations, alternative constructions, and equivalentsare also encompassed within the scope of the disclosure. Embodiments arenot restricted to operation within certain specific data processingenvironments, but are free to operate within a plurality of dataprocessing environments. Additionally, although embodiments have beendescribed using a particular series of transactions and steps, it shouldbe apparent to those skilled in the art that the scope of the presentdisclosure is not limited to the described series of transactions andsteps. Various features and aspects of the above-described embodimentsmay be used individually or jointly.

Further, while embodiments have been described using a particularcombination of hardware and software, it should be recognized that othercombinations of hardware and software are also within the scope of thepresent disclosure. Embodiments may be implemented only in hardware, oronly in software, or using combinations thereof. The various processesdescribed herein can be implemented on the same processor or differentprocessors in any combination. Accordingly, where components or modulesare described as being configured to perform certain operations, suchconfiguration can be accomplished, e.g., by designing electroniccircuits to perform the operation, by programming programmableelectronic circuits (such as microprocessors) to perform the operation,or any combination thereof. Processes can communicate using a variety oftechniques including but not limited to conventional techniques forinter process communication, and different pairs of processes may usedifferent techniques, or the same pair of processes may use differenttechniques at different times.

The specification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense. It will, however, beevident that additions, subtractions, deletions, and other modificationsand changes may be made thereunto without departing from the broaderspirit and scope as set forth in the claims. Thus, although specificdisclosure embodiments have been described, these are not intended to belimiting. Various modifications and equivalents are within the scope ofthe following claims.

The use of the terms “a” and “an” and “the” and similar referents in thecontext of describing the disclosed embodiments (especially in thecontext of the following claims) are to be construed to cover both thesingular and the plural, unless otherwise indicated herein or clearlycontradicted by context. The terms “comprising,” “having,” “including,”and “containing” are to be construed as open-ended terms (i.e., meaning“including, but not limited to,”) unless otherwise noted. The term“connected” is to be construed as partly or wholly contained within,attached to, or joined together, even if there is something intervening.Recitation of ranges of values herein are merely intended to serve as ashorthand method of referring individually to each separate valuefalling within the range, unless otherwise indicated herein and eachseparate value is incorporated into the specification as if it wereindividually recited herein. All methods described herein can beperformed in any suitable order unless otherwise indicated herein orotherwise clearly contradicted by context. The use of any and allexamples, or exemplary language (e.g., “such as”) provided herein, isintended merely to better illuminate embodiments and does not pose alimitation on the scope of the disclosure unless otherwise claimed. Nolanguage in the specification should be construed as indicating anynon-claimed element as essential to the practice of the disclosure.

Disjunctive language such as the phrase “at least one of X, Y, or Z,”unless specifically stated otherwise, is intended to be understoodwithin the context as used in general to present that an item, term,etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y,and/or Z). Thus, such disjunctive language is not generally intended to,and should not, imply that certain embodiments require at least one ofX, at least one of Y, or at least one of Z to each be present.

Preferred embodiments of this disclosure are described herein, includingthe best mode known for carrying out the disclosure. Variations of thosepreferred embodiments may become apparent to those of ordinary skill inthe art upon reading the foregoing description. Those of ordinary skillshould be able to employ such variations as appropriate and thedisclosure may be practiced otherwise than as specifically describedherein. Accordingly, this disclosure includes all modifications andequivalents of the subject matter recited in the claims appended heretoas permitted by applicable law. Moreover, any combination of theabove-described elements in all possible variations thereof isencompassed by the disclosure unless otherwise indicated herein.

All references, including publications, patent applications, andpatents, cited herein are hereby incorporated by reference to the sameextent as if each reference were individually and specifically indicatedto be incorporated by reference and were set forth in its entiretyherein.

In the foregoing specification, aspects of the disclosure are describedwith reference to specific embodiments thereof, but those skilled in theart will recognize that the disclosure is not limited thereto. Variousfeatures and aspects of the above-described disclosure may be usedindividually or jointly. Further, embodiments can be utilized in anynumber of environments and applications beyond those described hereinwithout departing from the broader spirit and scope of thespecification. The specification and drawings are, accordingly, to beregarded as illustrative rather than restrictive.

1. A method for coordinating change data across a plurality ofnon-Structured Query Language (NoSQL) data storage nodes, the methodcomprising: obtaining, from each of a plurality of client nodes in anetwork environment, a time to obtain data packets from each of theplurality of NoSQL data storage nodes; identifying, from the obtainedtimes to obtain data packets, a maximum time duration for each of theplurality of NoSQL data storage nodes to provide the data packets to anyof the plurality of client nodes; selecting a first NoSQL data storagenode as an optimized data storage node by determining that the firstNoSQL data storage node has a lowest maximum time duration to providethe data packets to any client node, wherein a specialized node isestablished at a computing device collocated with the first NoSQL datastorage node; obtaining change data from a first client node of theplurality of client nodes; forwarding the change data to the first NoSQLdata storage node, the first NoSQL data storage node configured toprovide the change data to the specialized node containing a hash mapconfigured to map change data; and obtaining a change confirmation fromthe specialized node via the first NoSQL data storage node, the changeconfirmation identifying that the change data has been updated to thehash map.
 2. The method of claim 1, wherein the change data is obtainedfrom a second NoSQL data storage node of the plurality of NoSQL datastorage nodes.
 3. The method of claim 1, wherein the network environmentincludes a planet-scale network environment.
 4. The method of claim 1,wherein the change data comprises change data capture (CDC) data.
 5. Themethod of claim 1, further comprising: forwarding the changeconfirmation to a third NoSQL data storage node coordinate the changedata across the plurality of NoSQL data storage nodes.
 6. The method ofclaim 1, further comprising: detecting a triggering event relating tothe first NoSQL data storage node; responsive to detecting thetriggering event, obtaining, from each of the plurality of client nodes,updated times to obtain data packets from each of the plurality of NoSQLdata storage nodes; identifying, from the obtained times to obtain datapackets, updated maximum time durations for each of the plurality ofNoSQL data storage nodes to provide the data packets to each clientnode; and selecting a fourth NoSQL data storage node as the optimizeddata storage node responsive to determining that the fourth NoSQL datastorage node has the lowest maximum time duration.
 7. The method ofclaim 1, wherein the specialized node is a change data capture node, andwherein the specialized node is configured to periodically transfer aportion of hash map data to a disk persistent store and remove theportion of the hash map data from the hash map.
 8. A data storage nodecomprising: a processor; and a computer-readable medium includinginstructions that, when executed by the processor, cause the processorto: establish a specialized node collocated with the data storage node,the data storage node selected as an optimized data storage node; obtainchange data capture (CDC) data from a first client node of a pluralityof client nodes; forward the CDC data to the specialized node to updatea hash map of change data with the obtained CDC data; receive a changeconfirmation from the specialized node, the change confirmationidentifying that the CDC data has been included in the hash map; andprovide the change confirmation to other data storage nodes of aplurality of data storage nodes for coordination of the CDC data acrossthe plurality of data storage nodes.
 9. The data storage node of claim8, wherein the non-transitory computer-readable medium further causesthe processor to: obtain, from each of the plurality of client nodes, atime to obtain data packets from each of the plurality of data storagenodes; identify, from the obtained times to obtain data packets, amaximum time duration for each of the plurality of data storage nodes toprovide the data packets to each client node; select the data storagenode as the optimized data storage node responsive to determining thatthe data storage node has a lowest maximum time duration.
 10. The datastorage node of claim 8, wherein the specialized node is a change datacapture (CDC) node, and wherein the specialized node is configured toperiodically transfer a portion of hash map data to a disk persistentstore and remove the portion of the hash map data from the hash map. 11.The data storage node of claim 10, wherein the portion of hash map datais transferred to the disk persistent store using least recently used(LRU) data replacement.
 12. A non-transitory computer-readable mediumincluding stored thereon a sequence of instructions which, when executedby a processor causes the processor to execute a process, the processcomprising: receiving, from each of a plurality of client nodes in anetwork environment, a time to obtain data packets from each of aplurality of NoSQL data storage nodes in the network environment;deriving, for each of the plurality of NoSQL data storage nodes, a timemetric indicative a time to provide data packets to each of theplurality of client nodes; selecting a first NoSQL data storage node asan optimized data storage node based at least in part on the timemetrics, wherein a specialized node is established at a computing devicecollocated with the first NoSQL data storage node; obtaining change datafrom a first client node of the plurality of client nodes; forwardingthe change data to the first NoSQL data storage node, the first NoSQLdata storage node configured to provide the change data to thespecialized node containing a hash map mapping all change data; andobtaining a change confirmation from the specialized node via the firstNoSQL data storage node, the change confirmation identifying that thechange data has been updated to the hash map.
 13. The non-transitorycomputer-readable medium of claim 12, wherein the time metric for eachplurality of NoSQL data storage nodes indicates a maximum time durationto provide data packets to any of the plurality of client nodes, whereinselecting the first NoSQL data storage node as the optimized datastorage node includes identifying that a first time metric for the firstNoSQL data storage node includes a lowest maximum time duration relativeto any other time metric for the plurality of NoSQL data storage nodes.14. The non-transitory computer-readable medium of claim 12, wherein thechange data is obtained from an intermediate NoSQL data storage node ofthe plurality of NoSQL data storage nodes.
 15. The non-transitorycomputer-readable medium of claim 12, wherein the network environmentincludes a planet-scale network environment.
 16. The non-transitorycomputer-readable medium of claim 12, wherein the change data compriseschange data capture (CDC) data.
 17. The non-transitory computer-readablemedium of claim 12, wherein the process further comprises: forwardingthe change confirmation to a second NoSQL data storage node toconsistently provide the change data across the plurality of NoSQL datastorage nodes.
 18. The non-transitory computer-readable medium of claim12, wherein the process further comprises: detecting a triggering eventrelating to the first NoSQL data storage node; responsive to detectingthe triggering event, obtaining, from each of the plurality of clientnodes, updated times to obtain data packets from each of the pluralityof NoSQL data storage nodes; identifying, from the obtained times toobtain data packets, updated maximum time durations for each of theplurality of NoSQL data storage nodes to provide the data packets toeach client node; and selecting a third NoSQL data storage node as theoptimized data storage node responsive to determining that the thirdNoSQL data storage node has the lowest maximum time duration.
 19. Thenon-transitory computer-readable medium of claim 18, wherein thetriggering event includes any of detecting that a new client node isincluded in the network environment or determining that a time periodfor periodically updating the optimized data storage node has expired.20. The non-transitory computer-readable medium of claim 12, wherein thespecialized node is a change data capture node, and wherein thespecialized node is configured to periodically transfer a portion ofhash map data to a disk persistent store and remove the portion of thehash map data from the hash map using least recently used (LRU) datareplacement.